As it is known by the man skilled in the art most of the existing centralized wireless networks operate in a single-hop fashion. There exist also few wireless networks operating in a multi-hop fashion, but they implement distributed scheduling mechanisms and distributed MAC protocols that are based on random access and that have sub-optimal interference estimation capabilities. There may be interference between two nodes when these nodes transmit simultaneously at certain data rates on the same radio channel.
It is recalled that a multi-hop wireless network is a network comprising a wireless backbone that is formed by wireless mesh access points (MAPs) interconnected with wireless links, each MAP being potentially capable of serving users (or client) wireless communication equipments. In contrast, a single-hop wireless network is a network comprising, notably, base stations or access points (or APs—or any equivalent radio network equipments) interconnected with a wired network backbone and serving user (or client) wireless communication equipments. Thus, a single-hop wireless network is a special case of a multi-hop wireless network.
In a centralized multi-hop wireless network it is important to be able to accurately estimate interference and to use this interference information to perform optimal scheduling decisions for the wireless backbone and wireless users. In centralized single-hop wireless networks it is also crucial to perform optimal scheduling decisions, but only the wireless interference among users of different access points must be preferably taken into account. However, this is not always the case. Indeed, in cellular centralized TDMA wireless systems, each base station schedules independently its user wireless communication equipments and the interference among them is not taken into account. In 802.11-based centralized wireless systems for enterprise networks, there also exist some approaches which try to take into account interference among user wireless communication equipments of different access points.
Several techniques may be used for scheduling transmissions in centralized multi-hop wireless networks. Most of these techniques use scheduling algorithms while assuming that the interference patterns are known. For instance, given a conflict graph representing conflict relationships between links, it can be shown that a maximum-weight independent set computation (or MWIS) on this conflict graph enables the network to achieve a maximum throughput. Unfortunately this optimal scheduling mechanism assumes that the conflict graph is static and already given and accurately represents interference relationships in the network, which is often inexact or even false. Indeed, in practice, it is first necessary to measure interference between nodes and then to adjust the interference model to the current conditions.
Several solutions may be implemented to measure link interferences. Pure measurement solutions are the most accurate, but they require an exponential number of measurements in the network. Some other solutions use interference models based on hop count or Received Signal Strength (or RSS) and Signal-to-Noise Ratio (or SNR) information or else Signal-to-Interference Ratio (or SIR) information. The last one (with SIR information) is the most accurate (a group of transmissions is considered as successful if all the concerned receivers operate with a SIR greater than a SIR threshold). While the SIR model has been used in theoretical studies and scheduling mechanisms, it has not yet been implemented in real centralized multi-hop wireless networks. Indeed, in these real multi-hop wireless networks measurement limitations prevent nodes from measuring all signals and therefore there exist hidden interferers that induce link failures and serious performance issues (such as severe packet loss and/or complete failure of wireless links).
Several interference estimation mechanisms have been proposed for WLAN networks. They are notably described in the following documents:                “DIRC: Increasing Indoor Wireless Capacity Using Directional Antennas”, X. Liu et al., ACM Sigcomm, Barcelona, Spain, August 2009,        “Measurement-based Approach to Modeling Link Capacity in 802.11-based Wireless networks”, A. Kashyap et al., Proc. ACM MobiCom, Montreal, Canada, October 2007,        “Online Estimation of RF Interference”, N. Ahmed et al., ACM CoNEXT, Madrid, Spain, December 2008,        “Analyzing the mac-level behavior of wireless networks in the wild”, R. Mahajan et al., Proc. ACM SIGCOMM, Pisa, Italy, August 2006,        “A general model of wireless interference”, L. Qiu et al., Proc. ACM MobiCom, Montreal, Canada, October 2007,        “Estimation of Link Interference in Static Multi-hop Wireless Networks”, J. Padhye et al., Proc. ACM Internet Measurement Conference (IMC), Berkeley, Calif., USA, October 2005,        “Harnessing exposed terminals in wireless networks”, M. Vutukuru et al., Proc. NSDI, San Francisco, Calif., April 2008.        
These mechanisms vary in accuracy and may be classified as online, offline, passive, or active. Offline techniques (such as the ones proposed by R. Mahajan et al. and J. Padhye et al.) are not applicable to scheduling algorithms which require real-time (online) interference estimation. Online techniques (such as the ones proposed by X. Liu et al. and N. Ahmed et al.) are not applicable to multi-hop wireless networks; rather they are restricted to computing conflict graphs for single-hop downlink traffic in WLANs. Moreover these online techniques do not allow to compute an optimal schedule and do not support detection of hidden interferers and refinement of conflict graph. A technique such as the one proposed by L. Qiu can be implemented into multi-hop wireless networks. It uses existing RSS-based interference mechanisms for Packet Delivery Ratio (or PDR) prediction that are based on offline calibration of SNR thresholds at different locations under no interference. However, one may show that, under interference, SNR calibration techniques can be highly inaccurate and then complementary mechanisms are needed. Passive techniques (such as the one proposed by M. Vutukuru et al.) disable carrier sense whenever possible but cannot detect hidden interferers.